Neurodegenerative diseases are the most common cause of dementia. Although their
underlying molecular pathologies have been identified, there is substantial heterogeneity in …

Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …

Despite significant improvements in our understanding of brain diseases, many barriers
remain. Cognitive neuroscience faces four major challenges: complex structure–function …

Disease staging, whereby the spatial extent and load of brain pathology are used to
estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard …

Alzheimer's and related diseases are significant health issues of this era. The
interdisciplinary use of deep learning in this field has shown great promise and gathered …

Dysexecutive Alzheimer's disease (dAD) manifests as a progressive dysexecutive syndrome
without prominent behavioral features, and previous studies suggest clinico-radiological …

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a
spectrum of cognitive-behavioural changes varying between individuals and over time …

Neurodegenerative diseases (NDDs) affect more than 50 million people worldwide, posing
a significant global health challenge as well as a high socioeconomic burden. With aging …

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical
models and human in vivo and post mortem studies. Understanding the consequences of …

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early
detection a challenge. Studies have attempted to combine biomarkers to improve AD …